We benchmark greedy weights against past techniques for computing minimax equilibria (in two-player zero-sum games), coarse-correlated equilibria, and correlated equilibria in large, randomly generated normal-form games. Additionally, we conduct experiments in subgames of Diplomacy, which has a long history as an important benchmark for AI research [1]}, [2]}, [3]}, [4]}, [5]} and has been a particularly active domain for research in recent years [6]}, [7]}, [8]}.
We find that greedy weights significantly improves the rate of convergence whenever sampling is used, in some cases by several orders of magnitude. Finally, we find that equilibria discovered by greedy weights in general-sum games typically have higher overall social welfare than those found by prior methods.
